Common Voice Spontaneous Speech 2.0 - Sabah Malay

Sabah Malay — Sabah Malay (msi)

This datasheet has been generated automatically, we would love to include more information, if you would like to help out, get in touch!

This datasheet is for version 2.0 of the the Mozilla Common Voice Spontaneous Speech dataset for Sabah Malay (msi). The dataset contains 2277 clips representing 14 hours of recorded speech (1 hours validated) from 33 speakers.

Data splits for modelling

Transcriptions

• Prompts: 119

• Duration: 13:55:38 [h:m:s]

• Avg. Transcription Len: 218

• Avg. Duration: 22.02[s]

• Valid Duration: 70.02[s]

• Total hours: 13.93[h]

• Valid hours: 0.02[h]

Samples

Questions

There follows a randomly selected sample of questions used in the corpus.

Siapa pemuzik, penyanyi atau penulis lagu kegemaran kamu?
Apa musim atau variasi cuaca yang ada di negara kamu?
Apa kebimbangan kamu tentang telefon bimbit?
Macam mana perasaan kamu tentang berenang? kenapa?
Macam mana keberkesanan sistem sokongan sosial yang disediakan oleh kerajaan kamu?

Responses

There follows a randomly selected sample of transcribed responses from the corpus.

Pemuzik yang saya suka ialah [Um] siapa Siti Nurhalizah ya sebab ia mempunya [Um] apa ni mempunyai lagu yang baik-baik tentang tradisi dan boleh juga digunakan [Um] melalui lagunya menyampaikan kepada masyarakat-masyarakat macam dia buli [Um] apa macam  mau bagi contoh-contoh yang baik lah kepada generasi yang datang.
Variasi cuaca yang ada di negara kami iaitu [Um] ada dua variasi iaitu monson timur laut  sama itu monson barat daya lah itu lah yang memang ada di negara kami.
[noise] Kebimbangan saya tentang telefon bimbit ini adalah yang pertama sekali, penggunaan yang melampau. Sebab, saya perhatikan apabila kita terlalu menggunakan telefon bimbit dalam tempoh jangka masa yang lama kita akan mudah rasa penat dan juga lesu. Ataupun kita akan mudah cepat betul mengantuk jadi bila kita sudah mengantuk kita akan leka untuk melakukan aktiviti lai-
Berana-beranang adalah satu [Um] yang saya suka sebab di samping [Um] untuk senaman dan [Um] menenangkan diri.
Setakat ini sistem sokongan sosial yang disediakan kerajaan saya rasa masih kurang lagi kurang meluas digunakan seluruh rakyat mungkin faktor [Um] setempat, budaya faktor lokasi perlu diperbaiki.

Fields

Each row of a tsv file represents a single audio clip, and contains the following information:

• client_id - hashed UUID of a given user

• audio_id - numeric id for audio file

• audio_file - audio file name

• duration_ms - duration of audio in milliseconds

• prompt_id - numeric id for prompt

• prompt - question for user

• transcription - transcription of the audio response

• votes - number of people that who approved a given transcript

• age - age of the speaker1

• gender - gender of the speaker1

• language - language name

• split - for data modelling, which subset of the data does this clip pertain to

• char_per_sec - how many characters of transcription per second of audio

• quality_tags - some automated assessment of the transcription--audio pair, separated by |

  • transcription-length - character per second under 3 characters per second

  • speech-rate - characters per second over 30 characters per second

  • short-audio - audio length under 2 seconds

  • long-audio - audio length over 30 seconds

Get involved!

Community links

• Common Voice translators on Pontoon

Contribute

• Contribute questions

• Validate questions

• Answer questions

• Transcribe recordings

• Validate transcriptions

Acknowledgements

Funding

This dataset was partially funded by the Open Multilingual Speech Fund managed by Mozilla Common Voice.

Licence

This dataset is released under the Creative Commons Zero (CC-0) licence. By downloading this data you agree to not determine the identity of speakers in the dataset.

Footnotes

1. For a full list of age, gender, and accent options, see the demographics spec. These will only be reported if the speaker opted in to provide that information. ↩ ↩2